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The rapid convergence rate, high fidelity learning outcome and low computational cost are key targets in solving the learning problem of the complex physical system. Guided

Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results.

In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex
functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex.

Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation for HSR, which is a non-convex large-scale optimization problem. Our contributions contain both computational and theoretical aspects.

Convolutional sparse representations allow modeling an entire image as an alternative to the more common independent patch-based
formulations. Although many approaches have been proposed to efficiently solve the convolutional dictionary learning (CDL) problem,
their computational performance is constrained by the dictionary update stage. In this work, we include two improvements to existing

This work explores sequential Bayesian binary hypothesis testing in the social learning setup under expertise diversity. We consider a two-agent (say advisor-learner) sequential binary hypothesis test where the learner infers the hypothesis based on the decision of the advisor, a prior private signal, and individual belief. In addition, the agents have varying expertise, in terms of the noise variance in the private signal.